Kermt Add Cmim Pretrain
NVIDIAOfficialPublished by NVIDIA
- Skill
- Workflow
- Software development
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This workflow converts an existing grover_base checkpoint (encoder-only or with vocab heads) into a hybrid model by adding a randomly-initialized cMIM decoder and latent distribution. It then proceeds to continue pretraining on the user's provided corpus. This process allows users to incorporate contrastive objectives into their models without the computational cost of restarting pretraining from scratch.
What it does
- Validates the input checkpoint to ensure it is suitable for hybrid conversion.
- Prepares the training data and builds a fresh vocabulary from the user's corpus.
- Upgrades the checkpoint by adding a cMIM decoder and latent distribution.
- Launches a detached pretraining run using the newly created hybrid checkpoint.
- Provides audit logs and provenance for the upgraded checkpoint.
When to use it
- When you have a grover_base checkpoint and want to add SMILES-reconstruction contrastive objectives.
- When you want to extend an existing model's capabilities without performing a full from-scratch pretraining run.
- When you need to adapt a pretrained encoder to a new corpus while maintaining hybrid training objectives.
Advantages
- Saves significant time compared to restarting pretraining from scratch.
- Enables the use of contrastive learning objectives on existing encoder-only models.
- Maintains provenance by capturing upgrade summaries and input manifests.
- Ensures data integrity through automated validation of checkpoints and corpora.
Usage examples
- “Upgrade my grover_base checkpoint at ./models/base.pt to a hybrid model using my new dataset at ./data/corpus.csv.”
- The agent validates the checkpoint, prepares the data, creates an upgraded hybrid checkpoint, and initiates the continue-pretraining process.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/kermt-add-cmim-pretrain/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip kermt-add-cmim-pretrain.zip -d .agents/skills
Documentation by NVIDIA
kermt-add-cmim-pretrain
Convert a grover_base checkpoint (legacy original-GROVER grover.encoders.*
or modern kermt.encoders.*, with or without vocab heads) into a fully-formed
hybrid (cMIM + vocab) checkpoint, then continue pretraining on the user's
corpus as hybrid.
This is a thin wrapper: upgrade_to_hybrid.py produces a new ckpt that
classifies as model_type: hybrid via check_checkpoint.py, and the rest of
the workflow is identical to kermt-continue-pretrain.
Status: experimental. This workflow is functional end-to-end but has not been benchmarked against the manuscript's from-scratch hybrid training (which produces the released checkpoint). Use as an experimental alternative to
kermt-pretrain-scratchwhen you want to extend an existing grover_base checkpoint rather than restart from random init. Validate downstream performance on your own benchmark before relying on the upgraded ckpt for production work.
Skill and runtime paths
Set SKILL_DIR to the absolute path of this installed skill directory. Export
KERMT_REPO as the absolute path to the KERMT checkout used for model
execution. The bundled container helper mounts that checkout at
/workspace and this skill at /skill (read-only). Commands inside
the container use /skill/scripts/; defaults are bundled in config/.
Hardware requirements
Same as kermt-continue-pretrain (the cMIM decoder adds parameters but not
substantially; VRAM headroom should be fine). The upgrade step itself is
fast (~5 s) and CPU-only — only the subsequent continue-pretrain consumes
GPU.
When to invoke
User has a grover_base checkpoint (encoder-only or with vocab heads) and wants to extend it into a hybrid (vocab + cMIM contrastive) pretrain.
Useful for adding the SMILES-reconstruction contrastive objective to a pretrained encoder without restarting pretraining from scratch (which
kermt-pretrain-scratchwould do at days-scale).
For continuing an existing hybrid or cmim ckpt: use kermt-continue-pretrain
directly. For training a fresh model on a custom corpus: use
kermt-pretrain-scratch.
Inputs
Required:
--ckpt <path>— grover_base ckpt to upgrade. Validated viacheck_checkpoint.py --mode upgrade_to_hybrid; rejected if the ckpt already has a contrast head or task FFN.--csv <path>— pretrain corpus CSV. Same shape askermt-continue-pretrain's--csvinput.
Optional (same as kermt-continue-pretrain):
--val-csv <path>— separate validation CSV. Without it, prepare_data auto-splits by--val-frac 0.1.Training-hyperparameter overrides (
--epochs N,--batch-size N, lr triple,--warmup-epochs F, etc.).--vocab-loss-weight F/--latent-dim N/--contrastive-temperature F.--wandb-project NAME/--wandb-run-name NAME— optional Weights & Biases logging (run name honored only alongside a project). Off by default.--gpus 0,2.
Workflow
Let $KERMT_REPO be the path to your kermt repo checkout.
Pre-flight: check_system (same as
kermt-continue-pretrainstep 1).Compute run directory:
RUN_DIR=$KERMT_REPO/runs/add-cmim-pretrain_$(date -u +%Y-%m-%dT%H-%M-%SZ)Validate the input ckpt with
check_checkpoint --mode upgrade_to_hybrid. Abort onok: false. The validator rejects ckpts that already have contrast head (suggestkermt-continue-pretrain) or task FFN heads (the ckpt has been finetuned; suggest using the original pretrain checkpoint).Validate the corpus via
check_data --mode pretrain. Abort onok: false.Prepare the data with
--mode pretrain— without--vocab-dir. The upgrade builds fresh vocab heads sized to the corpus's vocab, so we wantprepare_datato produce a new vocab from the corpus rather than passing through the ckpt's old vocab (which may not even exist for encoder-only legacy grover_base ckpts):"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \ "python /skill/scripts/prepare_data.py --mode pretrain \\ --csv /data/<basename> --out /runs/data \\ [--val-csv /data/<val-basename>] [--val-frac 0.1] [--seed 0]"The output manifest has
vocab_source: "built_fresh"and includes asmiles_vocab(built from the corpus, needed for the new decoder).Upgrade the ckpt.
"$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> --run-dir $RUN_DIR -- \ "python /skill/scripts/upgrade_to_hybrid.py \\ --ckpt /ckpt \\ --prepare-manifest /runs/data/prepare_data.json \\ --out /runs/upgraded.pt"Surface the JSON summary to the user — especially
warnings[], which includes any encoder-arch drift notes (e.g. legacy GROVER had two extraact_func_*keys that modern KERMTEmbedding doesn't) and the pretrain_ddp.py--backboneargparse-restriction note if the upgraded ckpt's backbone is anything other thangtrans.Estimate runtime + confirm with the user. Same heuristic as
kermt-continue-pretrain(corpus size × epochs × GPU count → wall time).Launch the runner detached.
"$SKILL_DIR/scripts/kermt_container.sh" run_detached \\ --name kermt-add-cmim-pretrain-<ts> \\ --run-dir $RUN_DIR -- \\ "python /skill/scripts/run_pretrain_local.py \\ --ckpt /runs/upgraded.pt \\ --prepare-manifest /runs/data/prepare_data.json \\ --out /runs \\ [--epochs N --batch-size N ...]"The runner sees the upgraded ckpt as
model_type: hybrid, so it auto-dispatches--pretrain_mode hybrid --vocab_loss_weight 1.0with smiles_vocab plumbed through.Report to the user with the upgraded ckpt path + the same run.json pointer / log path / tensorboard URL pattern as
kermt-continue-pretrain.
Hard rules
Never modify the user's input ckpt. The upgrade writes a new file at
<run_dir>/upgraded.pt; the source ckpt stays untouched.Vocab heads are always fresh. Even if the input grover_base has vocab heads, they're discarded and rebuilt sized to the new corpus's vocab. Continue-pretraining the upgraded ckpt will train those new heads alongside the decoder.
Don't auto-relax
--backbonechoices. If the upgrade warning fires because the input ckpt's backbone isn'tgtrans(e.g. legacydualtrans), surface the warning and ask the user. Do NOT silently modify parsing.py to add the legacy backbone to the choices list.
Common errors
check_checkpoint rejected the ckptwith model_type=hybrid or cmim → user's ckpt already has a contrast head. Redirect tokermt-continue-pretrain.check_checkpoint rejected the ckptwith task_ffn=true → the ckpt has been finetuned. The upgrade workflow only supports pretrain checkpoints.prepare manifest missing smiles_vocab→ prepare_data was invoked with--skip-vocabor some equivalent that omitted the smiles vocab. Re-run prepare without those flags.unexpected key(s) in encoder loadwarning → legacy GROVER architectures saved a couple ofact_func_*weights that modern KERMTEmbedding doesn't use. Benign; the rest of the encoder loaded correctly.
What's in run.json after a successful run
Same reproducibility fields as kermt-continue-pretrain, plus the upgrade step's
summary.json is captured under the inputs.upgrade_summary path so the
provenance of the upgraded ckpt is auditable.
Replayability
Same as kermt-continue-pretrain: cmd_replay rebuilds the
run_pretrain_local.py --ckpt <upgraded.pt> ... invocation. To redo the
full add-cmim flow end-to-end, the user also needs the input grover_base
ckpt and the corpus — both are captured in the prepare_data and upgrade
manifests by absolute path.
Files
- ATTRIBUTION.mdAdded by AutoGineer853 B
- BENCHMARK.md7.6 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md8.4 KiB
- config/defaults_pretrain.json2.6 KiB
- evals/evals.json5.6 KiB
- scripts/_utils.pyScript14.1 KiB
- scripts/check_checkpoint.pyScript20.1 KiB
- scripts/check_data.pyScript11.9 KiB
- scripts/kermt_container.shScript18.9 KiB
- scripts/prepare_data.pyScript36.5 KiB
- scripts/run_pretrain_local.pyScript34.8 KiB
- scripts/upgrade_to_hybrid.pyScript18.2 KiB
- skill-card.md4.4 KiB
- skill.oms.sig6.3 KiB
scripts/kermt_container.sh
#!/usr/bin/env bash
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
# kermt_container.sh — bootstrap helper for the kermt agent skills.
#
# Two ways to use this file:
#
# 1. As a subcommand dispatcher (recommended for skills):
# "$SKILL_DIR/scripts/kermt_container.sh" ensure_image
# "$SKILL_DIR/scripts/kermt_container.sh" run --ckpt /host/ckpt.pt -- python -c 'import torch; print(torch.cuda.device_count())'
# "$SKILL_DIR/scripts/kermt_container.sh" run_detached --name foo --run-dir runs/foo -- bash train.sh
#
# 2. Sourced into a shell or another script, then call the kermt_* functions
# directly:
# source "$SKILL_DIR/scripts/kermt_container.sh"
# kermt_ensure_image
# kermt_run --ckpt /host/ckpt.pt -- python ...
#
# Configuration (override via env vars before invocation):
# KERMT_IMAGE docker image tag (default: kermt:latest)
# KERMT_REPO host path to the kermt repo checkout (default: auto-derived
# from this script's location)
# KERMT_GPUS value passed to docker --gpus (default: all)
#
# Mount flags accepted by kermt_run / kermt_run_detached:
# --data <path> bind to /data (read-only). If <path> is a file,
# its PARENT directory is mounted at /data so
# commands can use /data/<basename>; if <path> is a
# directory, it is mounted at /data directly.
# --ckpt <path> bind to /ckpt (read-only; the path is mounted as-is)
# --vocab-dir <dir> bind to /vocab (read-only)
# --run-dir <dir> bind to /runs (read-write; created on host if missing)
# --model-dir <dir> bind to /model (read-write; created on host if missing).
# Target for released-model downloads (fetch_released_model.py).
#
# Additional flags for kermt_run_detached:
# --name <name> docker container name (default: kermt-<UTC-timestamp>-<pid>)
#
# Everything after `--` is the command passed to the container. It runs inside
# the `kermt` conda environment (the image's default env).
set -o pipefail
: "${KERMT_IMAGE:=kermt:latest}"
: "${KERMT_GPUS:=all}"
# The skill may be installed outside the KERMT checkout. Mount its own helpers
# separately so container commands always execute the distributed skill copy.
_kermt_script_dir="$(cd "$(dirname "${BASH_SOURCE[0]:-$0}")" && pwd)"
_kermt_bundle_dir="$(cd "$_kermt_script_dir/.." && pwd)"
if [[ -z "${KERMT_REPO:-}" ]]; then
for _kermt_start in "$_kermt_script_dir" "$PWD"; do
_kermt_candidate="$_kermt_start"
while [[ "$_kermt_candidate" != / ]]; do
if [[ -f "$_kermt_candidate/main.py" && -d "$_kermt_candidate/kermt" ]]; then
KERMT_REPO="$_kermt_candidate"
break 2
fi
_kermt_candidate="$(dirname "$_kermt_candidate")"
done
done
unset _kermt_start _kermt_candidate
fi
unset _kermt_script_dir
_kermt_require_repo() {
if [[ -z "${KERMT_REPO:-}" || ! -f "$KERMT_REPO/main.py" || ! -d "$KERMT_REPO/kermt" ]]; then
echo "[kermt] Set KERMT_REPO to the KERMT checkout containing main.py and kermt/." >&2
return 1
fi
KERMT_REPO="$(cd "$KERMT_REPO" && pwd)" || return $?
export KERMT_REPO
}
# -----------------------------------------------------------------------------
# Host environment checks
# -----------------------------------------------------------------------------
kermt_check_docker() {
if ! command -v docker >/dev/null 2>&1; then
echo "[kermt] error: docker not found on PATH. Install Docker first." >&2
return 1
fi
if ! docker info >/dev/null 2>&1; then
echo "[kermt] error: docker daemon not reachable. Is the docker service running, and is your user in the 'docker' group?" >&2
return 1
fi
}
kermt_check_system() {
# Probe host system and report GPU presence + VRAM + compute capability +
# driver / CUDA version + disk space. Emits a single JSON document to
# stdout that the calling skill consumes; exits 0 with `ok: false` and a
# populated `gaps` array when anything is below the per-workflow minimum,
# exits 1 only on unexpected internal errors. Uses host nvidia-smi + df +
# host python3 (stdlib only).
python3 - "$KERMT_REPO" "$KERMT_IMAGE" <<'PYEOF'
import json, os, shutil, subprocess, sys
repo, image = sys.argv[1], sys.argv[2]
result = {
"ok": True,
"gpus": [],
"disk": {"path": repo, "free_gb": None, "min_gb": 20},
"host": {"docker": None, "nvidia_smi": None, "container_toolkit": None},
"image": {"tag": image, "present_locally": None},
"gaps": [],
}
def _gap(msg):
result["ok"] = False
result["gaps"].append(msg)
# docker presence
try:
r = subprocess.run(["docker", "info"], capture_output=True, text=True, timeout=10)
result["host"]["docker"] = "ok" if r.returncode == 0 else f"failed: {r.stderr.strip().splitlines()[-1] if r.stderr else 'unknown'}"
if r.returncode != 0:
_gap("docker daemon not reachable (is the service running, and is your user in the 'docker' group?)")
except FileNotFoundError:
result["host"]["docker"] = "not found"
_gap("docker not on PATH; install Docker first")
except Exception as e:
result["host"]["docker"] = f"error: {e}"
_gap(f"docker probe failed: {e}")
# nvidia-smi (host driver)
try:
r = subprocess.run(
["nvidia-smi", "--query-gpu=name,memory.total,compute_cap,driver_version,uuid",
"--format=csv,noheader,nounits"],
capture_output=True, text=True, timeout=10,
)
if r.returncode == 0:
result["host"]["nvidia_smi"] = "ok"
for line in r.stdout.strip().splitlines():
parts = [p.strip() for p in line.split(",")]
if len(parts) >= 5:
try:
vram_mb = int(parts[1])
except ValueError:
vram_mb = None
result["gpus"].append({
"name": parts[0],
"vram_mb": vram_mb,
"compute_cap": parts[2],
"driver": parts[3],
"uuid": parts[4],
})
if not result["gpus"]:
_gap("nvidia-smi succeeded but reported no GPUs")
else:
result["host"]["nvidia_smi"] = "failed"
_gap("nvidia-smi found but failed; is the NVIDIA driver loaded?")
except FileNotFoundError:
result["host"]["nvidia_smi"] = "not found"
_gap("nvidia-smi not on PATH; install the NVIDIA driver")
except Exception as e:
result["host"]["nvidia_smi"] = f"error: {e}"
_gap(f"nvidia-smi probe failed: {e}")
# disk free at the repo location
try:
free_bytes = shutil.disk_usage(repo).free
free_gb = free_bytes // (1024**3)
result["disk"]["free_gb"] = free_gb
if free_gb < result["disk"]["min_gb"]:
_gap(f"disk free at {repo} is {free_gb} GB; need at least {result['disk']['min_gb']} GB for the kermt image")
except Exception as e:
_gap(f"could not check disk space at {repo}: {e}")
# image presence (informational only)
try:
r = subprocess.run(["docker", "image", "inspect", image], capture_output=True, text=True, timeout=10)
result["image"]["present_locally"] = (r.returncode == 0)
except Exception:
result["image"]["present_locally"] = None
# nvidia-container-toolkit probe — only meaningful if both docker and a
# locally-present image are available. Pick kermt:$tag first; fall back to
# the small CUDA base image if that's the only one present; otherwise skip
# (avoid pulling anything).
def _probe_image():
for img in (image, "nvidia/cuda:12.6.3-base-ubuntu22.04"):
r = subprocess.run(["docker", "image", "inspect", img], capture_output=True)
if r.returncode == 0:
return img
return None
probe_img = _probe_image()
if probe_img:
try:
r = subprocess.run(
["docker", "run", "--rm", "--gpus", "all", probe_img, "nvidia-smi"],
capture_output=True, text=True, timeout=60,
)
if r.returncode == 0:
result["host"]["container_toolkit"] = f"ok (probed via {probe_img})"
else:
result["host"]["container_toolkit"] = f"failed (probed via {probe_img})"
_gap("`docker run --gpus all` failed; install nvidia-container-toolkit and ensure the host driver supports it")
except Exception as e:
result["host"]["container_toolkit"] = f"error: {e}"
_gap(f"nvidia-container-toolkit probe failed: {e}")
else:
result["host"]["container_toolkit"] = "skipped (no probe image present locally; run ensure_image first)"
print(json.dumps(result, indent=2))
PYEOF
}
kermt_check_gpu() {
# Probes whether `docker --gpus all` is wired up (nvidia-container-toolkit).
# Image-selection priority (never pulls anything):
# 1) $KERMT_IMAGE if it exists locally,
# 2) else nvidia/cuda:12.6.3-base-ubuntu22.04 if it exists locally,
# 3) else skip with a warning (return 0). The smoke test inside kermt_run
# will catch broken GPU passthrough later anyway.
local probe_img=""
if docker image inspect "$KERMT_IMAGE" >/dev/null 2>&1; then
probe_img="$KERMT_IMAGE"
elif docker image inspect nvidia/cuda:12.6.3-base-ubuntu22.04 >/dev/null 2>&1; then
probe_img="nvidia/cuda:12.6.3-base-ubuntu22.04"
else
echo "[kermt] check_gpu: skipped — neither '$KERMT_IMAGE' nor 'nvidia/cuda:12.6.3-base-ubuntu22.04' is present locally. Run 'ensure_image' first, or this probe will be exercised by the in-container smoke test." >&2
return 0
fi
if ! docker run --rm --gpus all "$probe_img" nvidia-smi >/dev/null 2>&1; then
echo "[kermt] error: 'docker run --gpus all' failed (probe image: $probe_img). Install nvidia-container-toolkit and ensure the host has a CUDA-capable NVIDIA driver." >&2
return 1
fi
}
# -----------------------------------------------------------------------------
# Image build / verification
# -----------------------------------------------------------------------------
kermt_ensure_image() {
_kermt_require_repo || return $?
kermt_check_docker || return $?
if docker image inspect "$KERMT_IMAGE" >/dev/null 2>&1; then
local id
id=$(docker image inspect "$KERMT_IMAGE" --format '{{.Id}}' 2>/dev/null | cut -c1-19)
echo "[kermt] image '$KERMT_IMAGE' already present (${id:-unknown})"
return 0
fi
echo "[kermt] image '$KERMT_IMAGE' not found; building from $KERMT_REPO/Dockerfile"
echo "[kermt] first build typically takes 10-20 minutes on a typical workstation; subsequent runs reuse the cached image"
docker build -t "$KERMT_IMAGE" -f "$KERMT_REPO/Dockerfile" "$KERMT_REPO"
}
# -----------------------------------------------------------------------------
# Mount-flag parser, internal
# -----------------------------------------------------------------------------
# Reads flags from the caller's positional args until it hits '--', appending
# `-v src:dst[:ro]` pairs into the caller-provided array name (passed as $1).
# Returns the number of caller-provided args consumed via _kermt_consumed.
# This is bash-specific (uses nameref via `declare -n`).
_kermt_parse_mounts() {
local -n _out="$1"
shift
_kermt_consumed=0
while [[ $# -gt 0 ]]; do
case "$1" in
--)
return 0
;;
--data)
[[ -e "$2" ]] || { echo "[kermt] --data path not found: $2" >&2; return 1; }
# If the user passes a file, mount its parent directory at /data so
# downstream commands can refer to /data/<basename>. Mounting a
# single file at /data makes the path-as-directory pattern in the
# skill examples (`--csv /data/<basename>`) fail with "not found".
if [[ -d "$2" ]]; then
_out+=("-v" "$(realpath "$2"):/data:ro")
else
_out+=("-v" "$(realpath "$(dirname "$2")"):/data:ro")
fi
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--ckpt)
[[ -e "$2" ]] || { echo "[kermt] --ckpt path not found: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/ckpt:ro")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--vocab-dir)
[[ -d "$2" ]] || { echo "[kermt] --vocab-dir not found or not a directory: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/vocab:ro")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--run-dir)
mkdir -p "$2" || { echo "[kermt] failed to create --run-dir: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/runs")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
--model-dir)
mkdir -p "$2" || { echo "[kermt] failed to create --model-dir: $2" >&2; return 1; }
_out+=("-v" "$(realpath "$2"):/model")
shift 2; _kermt_consumed=$((_kermt_consumed + 2))
;;
*)
return 0
;;
esac
done
}
# -----------------------------------------------------------------------------
# Foreground / detached run
# -----------------------------------------------------------------------------
# Capture host-side git state for the repo and emit `-e KERMT_REPO_COMMIT=…
# -e KERMT_REPO_DIRTY=true|false` flags. Used by the run / run_detached
# wrappers so the runner's run.json manifest gets honest commit info even
# though `git -C /workspace` inside the container fails due to bind-mount
# ownership.
_kermt_git_env_flags() {
local commit="unknown"
local dirty="false"
if command -v git >/dev/null 2>&1 && [[ -d "$KERMT_REPO/.git" ]]; then
local c
c=$(git -C "$KERMT_REPO" rev-parse HEAD 2>/dev/null) && commit="$c"
# `--untracked-files=no` filters out user-private notes (e.g. a CLAUDE.md
# or RELEASE_PLAN_v2.0.md at the repo root) that wouldn't affect
# reproducibility — only modifications to tracked files do.
if [[ -n "$(git -C "$KERMT_REPO" status --porcelain --untracked-files=no 2>/dev/null | head -n 1)" ]]; then
dirty="true"
fi
fi
printf '%s\n%s\n%s\n%s\n' "-e" "KERMT_REPO_COMMIT=$commit" "-e" "KERMT_REPO_DIRTY=$dirty"
}
# Forward HF_TOKEN into the container when it is set, so fetch_released_model.py
# can authenticate to Hugging Face. The current release is public (no token
# needed); this only guards against shared-IP rate limits or a future gated
# repo. Emits nothing when HF_TOKEN is unset.
_kermt_hf_env_flags() {
if [[ -n "${HF_TOKEN:-}" ]]; then
printf '%s\n%s\n' "-e" "HF_TOKEN=$HF_TOKEN"
fi
}
kermt_run() {
kermt_ensure_image || return $?
local mount_args=()
_kermt_parse_mounts mount_args "$@" || return $?
shift "$_kermt_consumed"
if [[ "${1:-}" != "--" ]]; then
echo "[kermt] expected '--' separating mount flags from the command (got '${1:-}')" >&2
return 1
fi
shift
if [[ $# -eq 0 ]]; then
echo "[kermt] no command supplied after '--'" >&2
return 1
fi
local git_args=()
while IFS= read -r line; do git_args+=("$line"); done < <(_kermt_git_env_flags)
local hf_args=()
while IFS= read -r line; do hf_args+=("$line"); done < <(_kermt_hf_env_flags)
docker run --rm --gpus "$KERMT_GPUS" \
--user "$(id -u):$(id -g)" \
-v "$KERMT_REPO:/workspace" \
-v "$_kermt_bundle_dir:/skill:ro" \
"${mount_args[@]}" \
-w /workspace \
-e KERMT_REPO=/workspace \
-e PYTHONPATH=/workspace \
-e HOME=/tmp/kermt-home \
"${git_args[@]}" \
"${hf_args[@]}" \
"$KERMT_IMAGE" \
conda run -n kermt --no-capture-output bash -c "$*"
}
kermt_run_detached() {
kermt_ensure_image || return $?
local name=""
local mount_args=()
# Pull --name out first, then let the shared mount parser handle the rest.
while [[ $# -gt 0 ]]; do
case "$1" in
--name) name="$2"; shift 2 ;;
--) break ;;
--data|--ckpt|--vocab-dir|--run-dir|--model-dir) break ;;
*) break ;;
esac
done
_kermt_parse_mounts mount_args "$@" || return $?
shift "$_kermt_consumed"
if [[ "${1:-}" != "--" ]]; then
echo "[kermt] expected '--' separating mount flags from the command (got '${1:-}')" >&2
return 1
fi
shift
if [[ $# -eq 0 ]]; then
echo "[kermt] no command supplied after '--'" >&2
return 1
fi
if [[ -z "$name" ]]; then
name="kermt-$(date -u +%Y%m%dT%H%M%SZ)-$$"
fi
local cid
local git_args=()
while IFS= read -r line; do git_args+=("$line"); done < <(_kermt_git_env_flags)
local hf_args=()
while IFS= read -r line; do hf_args+=("$line"); done < <(_kermt_hf_env_flags)
cid=$(docker run -d --gpus "$KERMT_GPUS" \
--user "$(id -u):$(id -g)" \
--name "$name" \
-v "$KERMT_REPO:/workspace" \
-v "$_kermt_bundle_dir:/skill:ro" \
"${mount_args[@]}" \
-w /workspace \
-e KERMT_REPO=/workspace \
-e PYTHONPATH=/workspace \
-e HOME=/tmp/kermt-home \
"${git_args[@]}" \
"${hf_args[@]}" \
"$KERMT_IMAGE" \
conda run -n kermt --no-capture-output bash -c "$*") || return $?
echo "[kermt] container started: name=$name id=$cid"
echo "[kermt] follow logs: docker logs -f $name"
echo "[kermt] wait for exit: docker wait $name"
echo "[kermt] stop: docker stop $name"
echo "$cid"
}
# -----------------------------------------------------------------------------
# Subcommand dispatch when invoked directly (not sourced)
# -----------------------------------------------------------------------------
if [[ "${BASH_SOURCE[0]:-$0}" == "${0}" ]]; then
cmd="${1:-}"; shift || true
case "$cmd" in
check_docker) kermt_check_docker "$@" ;;
check_gpu) kermt_check_gpu "$@" ;;
check_system) kermt_check_system "$@" ;;
ensure_image) kermt_ensure_image "$@" ;;
run) kermt_run "$@" ;;
run_detached) kermt_run_detached "$@" ;;
""|-h|--help)
cat >&2 <<EOF
usage: $0 <subcommand> [args...]
Subcommands:
check_docker Verify docker is installed and the daemon is reachable.
check_gpu Verify 'docker --gpus all' works (nvidia-container-toolkit).
check_system Emit a JSON probe of host GPU + VRAM + compute_cap +
driver + disk space + container toolkit + image presence.
Exits 0 with ok=false + a 'gaps' list when anything's
below the per-workflow minimum.
ensure_image Build kermt:latest from \$KERMT_REPO/Dockerfile if missing.
run [flags] -- ... Run a command inside the container (foreground, --rm).
run_detached [flags] -- ...
Run detached; prints container name + id + log hint.
Mount flags (for run / run_detached):
--data <path> bind to /data (read-only)
--ckpt <path> bind to /ckpt (read-only)
--vocab-dir <dir> bind to /vocab (read-only)
--run-dir <dir> bind to /runs (read-write; created on host if missing)
--model-dir <dir> bind to /model (read-write; released-model download target)
Additional flags for run_detached:
--name <name> container name (default: kermt-<timestamp>-<pid>)
Environment overrides:
KERMT_IMAGE default kermt:latest
KERMT_REPO checkout path; otherwise discovered above the skill or working directory
KERMT_GPUS default all
EOF
exit 1
;;
*)
echo "[kermt] unknown subcommand: $cmd" >&2
echo "[kermt] run '$0 --help' for usage" >&2
exit 1
;;
esac
fi
Requirements and compatibility
Written for: Claude Code, OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
The author says: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/bionemo-kermt-add-cmim-pretrain · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download kermt-add-cmim-pretrain.zip